HelloResearch

Opportunities for Undergraduate Research in Computer Science

October 26-28, 2018

Projects

There are twelve research projects, spanning a wide variety of computing related topics. These projects are designed and led by internationally recognized experts from academia and industry.

Indiana University Bloomington

Below are brief descriptions of the research projects we are offering for HelloResearch. If you are accepted into the workshop, you will have an opportunity to choose your top three projects that you most want to work on and we will do what we can to ensure that you are matched with one of your top choices.

Carlo Angiuli
Ph.D. Candidate
Computer Science
Carnegie Mellon University

Tori Lewis
Graduate Student
Computer Science
Indiana University

Sarah Spall
Graduate Student
Computer Science
Indiana University

1. Design and Implementation of Domain-Specific Programming Languages

We often think of programming languages as mostly interchangeable: a program written in Java could instead be written in C. However, narrow problem domains often benefit from small programming languages tailor-made to address those domains. Although we may not usually think of them as programs, Makefiles are just small programs that describe how to compile another program, and regular expressions are just small programs that accept or reject input strings. We will begin by exploring existing domain-specific languages and implementation strategies. Participants in this project will then design and implement a small domain-specific programming language, by embedding it in a functional programming language.

Research Area: Programming Languages
Location: Luddy 2069

Katy Börner
Distinguished Professor
Engineering & Information Science
Indiana University

Andreas Bueckle
Graduate Student
Information Science
Indiana University

Mike Hu
IoT Kit Assistant
Cyberinfrastructure for Network Science Center
Indiana University

2. Augmented Reality Visualizations of IoT Data

As the built environment becomes increasingly more complex and integrated with new technologies—including the emerging Internet of Things (IoT)—there is an urgent need to understand and communicate how embedded technologies affect the experience of individuals that inhabit these spaces. In this project, students will construct simple IoT setups using diverse sensors and actuators. Students will record and visualize IoT data using augmented reality data visualizations to explore, debug, and optimize IoT setups and their behavior.

Research Area: Sentient Architecture
Location: Luddy 4012

Jane Cleland-Huang
Professor
Computer Science and Engineering
University of Notre Dame

Michael Vierhauser
Post Doctoral Researcher
Computer Science and Engineering
University of Notre Dame

Shreya Kumar
Assistant Teaching Professor
Computer Science and Engineering
University of Notre Dame

3. Goal-Based Task Planning and Adaptation with Aerial Unmanned Vehicles

UAVs are increasingly deployed in environments where they must operate autonomously, possibly in cooperation with other UAVs, to achieve mission goals. In this project, we will take a goal-oriented approach to establish mission objectives and to dynamically plan and adapt UAV tasks using runtime monitoring and goal optimization techniques. Participants will write code to control UAVs and to develop their own mission planners. They will utilize an existing UAV framework for managing, coordinating, and monitoring UAV flights. Solutions will first be tested in a simulator and then (weather permitting) with physical UAVs.

Research Area: Software Engineering
Location: Info West 107 (Fri) and Luddy 0117 (Sat/Sun)

David Crandall
Associate Professor
Computer Science
Indiana University

Donald Williamson
Assistant Professor
Computer Science
Indiana University

4. Activity Recognition Through Deep Learning with Sound and Video

Virtual assistants such as Apple's Siri and Amazon's Alexa help us with daily tasks from checking the weather to controlling smart home devices like lights and door locks. But they're limited by the fact that they can't understand very much about what is going on around them; a user asking Alexa for “help” wants something very different depending on if they're walking into a dark room with an armload of groceries, trying to solve a crossword puzzle, or have fallen and broken a leg. Participants in this project will investigate combining visual data from cameras, audio data from microphones, and deep machine learning to recognize the activities that are going on near a smart device.

Research Area: Computer Vision and Audio Processing
Location: Luddy 4004

Kate Eddens
Assistant Research Scientist
Indiana University Network Science Institute

5. Usability Testing of a Data Collection Platform for Addressing Health Disparities

Personal social network data can be used to design, implement, and disseminate health behavior interventions; however, collecting this type of data is often tedious and time-consuming, and the interface design of most existing tools does not consider potential low literacy levels of target populations. Enso™ is an open source, mobile-ready, touch-screen data collection system that uses principles of clear health communication to collect and deliver accurate visual personal social network data for health assessment and intervention. In this project, students will design and implement usability testing of this mobile app, and may design any proposed improvements or changes to the app to maximize its effectiveness and satisfaction in low literacy populations.

Research Area: Health Communication Technology and Social Network Methods
Location: Luddy 4107

Funda Ergun
Professor
Computer Science
Indiana University

Saúl Blanco
Visiting Assistant Professor
Computer Science
Indiana University

Qin Zhang
Assistant Professor
Computer Science
Indiana University

Yuan Zhou
Assistant Professor
Computer Science
Indiana University

6. Resource-Efficient Algorithms for Big Data

How to efficiently process large datasets has become a central topic in a number of areas in computer science, including databases, machine learning and data mining. In this project participants are expected to design, implement, and evaluate algorithms for popular queries on large scale datasets using limited computational resources (space and time). For example, to find most frequent items in a long sequence of items (e.g., IP addresses, keywords), to estimate the average degree of nodes in a large graph (e.g., social network, web graph), etc.

Research Area: Big Data Algorithms
Location: Luddy 3069

7. Online Learning for Decision Making

Online learning for decision making encompasses problems where the learner makes sequential decisions based on the information learned from outcomes of previous decisions. It is formulated to widely address the conundrums encountered in various practical settings, e.g., online advertising, marketing and revenue management, mobile health, and has broader impacts on deep reinforcement learning and etc. Participating students are expected to design, implement, and evaluate algorithms for online learning and decision making in several concrete and practical settings, e.g., to select online advertisements to maximize the click-through rate, to learn the price-demand relation for a specific commodity and decide the pricing strategy to maximize the sales revenue, etc.

Research Area: Online Data Algorithms
Location: Luddy 1069

Raquel Hill
Associate Professor
Computer Science
Indiana University

Omkar Bhide
Research Assistant
Computer Science
Indiana University

8. Unmerited Trust: Exploring Data Sharing Practices of Android Applications

Smart phones include sensors that collect a wide range of information, including GPS coordinates, activity levels, and heart rate. In addition to this information, smartphone apps collect and share identifying information such as phone id, email addresses, contact lists, etc. While they have become an integral part of our lives, these devices and their apps may present a tremendous risk to user privacy. During this project, we will explore the privacy risks of smart phones by identifying apps that leak information, evaluating the data sharing eco-system, and evaluating the associated privacy policies for evidence of compliance.

Research Area: Security and Privacy
Location: Luddy 0006

Sarah Loos
Software Engineer
Google AI

Jeremy Karnowski
Director of Product, AI
Insight Data

9. Deep Learning Educational Content

Learning Equality is a non-profit that brings online educational resources to classrooms around the world with little or no internet connection. Teachers use Learning Equality's content to augment their existing lesson plans. The problem is that it is too difficult to sift through all the available content to find the lesson plans that are aligned with their own curriculum. Deep learning is a cutting edge technique that companies have used for many related tasks, like translating between languages. Participants in this project will use deep learning to assist educators on Learning Equality's platform by aligning educational content from a variety of K-16 STEM curricula.

Research Area: Deep Neural Networks
Location: Luddy 4105

Andrew Miller
Assistant Professor
Human-Centered Computing
Indiana University-Purdue University Indianapolis (IUPUI)

10. Designing Hospital Information Systems of the Future: Connecting Patients, Providers, and Family

Although hospital care is carefully documented and that information is electronically available to clinicians, few information systems exist for patients and their families to use while they are in the hospital. Information often appears trapped within the hospital room. In this research project, we will use Human-Centered Computing research and design techniques to envision the hospital information systems of the future. Using the real-life experiences of patients and their loved ones, we’ll radically re-think the design of patient-facing hospital technology. Participating researchers (that’s you!) will transform qualitative findings into design requirements, and propose audacious design solutions using interactive prototyping toolkits.

Research Area: Health Informatics
Location: Luddy 4101

Katie Siek
Associate Professor
Informatics
Indiana University

Cassie Kresnye
Research Assistant
Informatics
Indiana University

Susan Monsey
Undergraduate Student
Computer Science
Indiana University

11. Creating Custom Technology to Improve One's Quality of Life

Technology surrounds us — from the computer in your microwave to the speaking digital assistant on your phone, however these commodity technologies are created in a one-size-fits-some style that people appropriate into their everyday lives. In this research project, we will reflect on personas of underserved communities in technology — from rural older adults to pregnant teens to low socioeconomic status children — to develop custom, interactive technology to assist them improve their quality of life. In this workshop, we will explore barriers the target populations face, brainstorm sociotechnical solutions, and bring the solutions to reality. Aspiring researchers will learn how to distill design guidelines from qualitative data; prototype physical, interactive systems; 3D print and laser cut; and program embedded systems.

Research Area: Sociotechnical Systems
Location: Luddy 4010 & 4150 (Fab Lab)

Sid Stamm
Associate Professor
Computer Science
Rose-Hulman Institute of Technology

Lixing Song
Assistant Professor
Computer Science
Rose-Hulman Institute of Technology

Connor Bade
Undergraduate Student
Software Engineering
Rose-Hulman Institute of Technology

12. Wireless Device Privacy

You probably carry a smartphone in your pocket, and possibly a fitbit, smart watch, key locator tag or other devices that emit wireless signals. These signals can be used to track you across time and space. Aspiring researchers will learn how to carve a manageable research problem from a giant project, examine wireless signals and specifications for sources of private data, build a 'fingerprint' from a set of non-personal data, and read wireless signals to approximate location of a smartphone.

Research Area: Internet Privacy
Location: Luddy 4069

Carlo Angiuli Ph.D. Candidate Computer Science Carnegie Mellon University	Tori Lewis Graduate Student Computer Science Indiana University

Sarah Spall Graduate Student Computer Science Indiana University

1. Design and Implementation of Domain-Specific Programming Languages
We often think of programming languages as mostly interchangeable: a program written in Java could instead be written in C. However, narrow problem domains often benefit from small programming languages tailor-made to address those domains. Although we may not usually think of them as programs, Makefiles are just small programs that describe how to compile another program, and regular expressions are just small programs that accept or reject input strings. We will begin by exploring existing domain-specific languages and implementation strategies. Participants in this project will then design and implement a small domain-specific programming language, by embedding it in a functional programming language.
Research Area: Programming Languages Location: Luddy 2069

Katy Börner Distinguished Professor Engineering & Information Science Indiana University	Andreas Bueckle Graduate Student Information Science Indiana University

Mike Hu IoT Kit Assistant Cyberinfrastructure for Network Science Center Indiana University

2. Augmented Reality Visualizations of IoT Data
As the built environment becomes increasingly more complex and integrated with new technologies—including the emerging Internet of Things (IoT)—there is an urgent need to understand and communicate how embedded technologies affect the experience of individuals that inhabit these spaces. In this project, students will construct simple IoT setups using diverse sensors and actuators. Students will record and visualize IoT data using augmented reality data visualizations to explore, debug, and optimize IoT setups and their behavior.
Research Area: Sentient Architecture Location: Luddy 4012

Jane Cleland-Huang Professor Computer Science and Engineering University of Notre Dame	Michael Vierhauser Post Doctoral Researcher Computer Science and Engineering University of Notre Dame

Shreya Kumar Assistant Teaching Professor Computer Science and Engineering University of Notre Dame

3. Goal-Based Task Planning and Adaptation with Aerial Unmanned Vehicles
UAVs are increasingly deployed in environments where they must operate autonomously, possibly in cooperation with other UAVs, to achieve mission goals. In this project, we will take a goal-oriented approach to establish mission objectives and to dynamically plan and adapt UAV tasks using runtime monitoring and goal optimization techniques. Participants will write code to control UAVs and to develop their own mission planners. They will utilize an existing UAV framework for managing, coordinating, and monitoring UAV flights. Solutions will first be tested in a simulator and then (weather permitting) with physical UAVs.
Research Area: Software Engineering Location: Info West 107 (Fri) and Luddy 0117 (Sat/Sun)

David Crandall Associate Professor Computer Science Indiana University	Donald Williamson Assistant Professor Computer Science Indiana University
4. Activity Recognition Through Deep Learning with Sound and Video
Virtual assistants such as Apple's Siri and Amazon's Alexa help us with daily tasks from checking the weather to controlling smart home devices like lights and door locks. But they're limited by the fact that they can't understand very much about what is going on around them; a user asking Alexa for “help” wants something very different depending on if they're walking into a dark room with an armload of groceries, trying to solve a crossword puzzle, or have fallen and broken a leg. Participants in this project will investigate combining visual data from cameras, audio data from microphones, and deep machine learning to recognize the activities that are going on near a smart device.
Research Area: Computer Vision and Audio Processing Location: Luddy 4004

Kate Eddens Assistant Research Scientist Indiana University Network Science Institute

5. Usability Testing of a Data Collection Platform for Addressing Health Disparities
Personal social network data can be used to design, implement, and disseminate health behavior interventions; however, collecting this type of data is often tedious and time-consuming, and the interface design of most existing tools does not consider potential low literacy levels of target populations. Enso™ is an open source, mobile-ready, touch-screen data collection system that uses principles of clear health communication to collect and deliver accurate visual personal social network data for health assessment and intervention. In this project, students will design and implement usability testing of this mobile app, and may design any proposed improvements or changes to the app to maximize its effectiveness and satisfaction in low literacy populations.
Research Area: Health Communication Technology and Social Network Methods Location: Luddy 4107

Funda Ergun Professor Computer Science Indiana University	Saúl Blanco Visiting Assistant Professor Computer Science Indiana University

Qin Zhang Assistant Professor Computer Science Indiana University	Yuan Zhou Assistant Professor Computer Science Indiana University

6. Resource-Efficient Algorithms for Big Data
How to efficiently process large datasets has become a central topic in a number of areas in computer science, including databases, machine learning and data mining. In this project participants are expected to design, implement, and evaluate algorithms for popular queries on large scale datasets using limited computational resources (space and time). For example, to find most frequent items in a long sequence of items (e.g., IP addresses, keywords), to estimate the average degree of nodes in a large graph (e.g., social network, web graph), etc.
Research Area: Big Data Algorithms Location: Luddy 3069
7. Online Learning for Decision Making
Online learning for decision making encompasses problems where the learner makes sequential decisions based on the information learned from outcomes of previous decisions. It is formulated to widely address the conundrums encountered in various practical settings, e.g., online advertising, marketing and revenue management, mobile health, and has broader impacts on deep reinforcement learning and etc. Participating students are expected to design, implement, and evaluate algorithms for online learning and decision making in several concrete and practical settings, e.g., to select online advertisements to maximize the click-through rate, to learn the price-demand relation for a specific commodity and decide the pricing strategy to maximize the sales revenue, etc.
Research Area: Online Data Algorithms Location: Luddy 1069

Raquel Hill Associate Professor Computer Science Indiana University	Omkar Bhide Research Assistant Computer Science Indiana University

8. Unmerited Trust: Exploring Data Sharing Practices of Android Applications
Smart phones include sensors that collect a wide range of information, including GPS coordinates, activity levels, and heart rate. In addition to this information, smartphone apps collect and share identifying information such as phone id, email addresses, contact lists, etc. While they have become an integral part of our lives, these devices and their apps may present a tremendous risk to user privacy. During this project, we will explore the privacy risks of smart phones by identifying apps that leak information, evaluating the data sharing eco-system, and evaluating the associated privacy policies for evidence of compliance.
Research Area: Security and Privacy Location: Luddy 0006

Sarah Loos Software Engineer Google AI	Jeremy Karnowski Director of Product, AI Insight Data

9. Deep Learning Educational Content
Learning Equality is a non-profit that brings online educational resources to classrooms around the world with little or no internet connection. Teachers use Learning Equality's content to augment their existing lesson plans. The problem is that it is too difficult to sift through all the available content to find the lesson plans that are aligned with their own curriculum. Deep learning is a cutting edge technique that companies have used for many related tasks, like translating between languages. Participants in this project will use deep learning to assist educators on Learning Equality's platform by aligning educational content from a variety of K-16 STEM curricula.
Research Area: Deep Neural Networks Location: Luddy 4105

Andrew Miller Assistant Professor Human-Centered Computing Indiana University-Purdue University Indianapolis (IUPUI)

10. Designing Hospital Information Systems of the Future: Connecting Patients, Providers, and Family
Although hospital care is carefully documented and that information is electronically available to clinicians, few information systems exist for patients and their families to use while they are in the hospital. Information often appears trapped within the hospital room. In this research project, we will use Human-Centered Computing research and design techniques to envision the hospital information systems of the future. Using the real-life experiences of patients and their loved ones, we’ll radically re-think the design of patient-facing hospital technology. Participating researchers (that’s you!) will transform qualitative findings into design requirements, and propose audacious design solutions using interactive prototyping toolkits.
Research Area: Health Informatics Location: Luddy 4101

Katie Siek Associate Professor Informatics Indiana University	Cassie Kresnye Research Assistant Informatics Indiana University

Susan Monsey Undergraduate Student Computer Science Indiana University

11. Creating Custom Technology to Improve One's Quality of Life
Technology surrounds us — from the computer in your microwave to the speaking digital assistant on your phone, however these commodity technologies are created in a one-size-fits-some style that people appropriate into their everyday lives. In this research project, we will reflect on personas of underserved communities in technology — from rural older adults to pregnant teens to low socioeconomic status children — to develop custom, interactive technology to assist them improve their quality of life. In this workshop, we will explore barriers the target populations face, brainstorm sociotechnical solutions, and bring the solutions to reality. Aspiring researchers will learn how to distill design guidelines from qualitative data; prototype physical, interactive systems; 3D print and laser cut; and program embedded systems.
Research Area: Sociotechnical Systems Location: Luddy 4010 & 4150 (Fab Lab)

Sid Stamm Associate Professor Computer Science Rose-Hulman Institute of Technology	Lixing Song Assistant Professor Computer Science Rose-Hulman Institute of Technology

Connor Bade Undergraduate Student Software Engineering Rose-Hulman Institute of Technology

12. Wireless Device Privacy
You probably carry a smartphone in your pocket, and possibly a fitbit, smart watch, key locator tag or other devices that emit wireless signals. These signals can be used to track you across time and space. Aspiring researchers will learn how to carve a manageable research problem from a giant project, examine wireless signals and specifications for sources of private data, build a 'fingerprint' from a set of non-personal data, and read wireless signals to approximate location of a smartphone.
Research Area: Internet Privacy Location: Luddy 4069